Title: AI Syllabus - Google Reichman AI Tech School

URL Source: https://graitechschool.co.il/product-management-syllabus

Markdown Content:
*changes may apply

## AI and Deep Learning Models Development

![Image 1](https://www.graitechschool.co.il/wp-content/uploads/2023/02/Group-11870.png)

![Image 2](https://www.graitechschool.co.il/wp-content/uploads/2022/12/Gilad-Kavalerchik__221220_67.jpg)

Introduction

*   Gemini, ChatGPT and other impressive artificial models.
*   Human and Artificial Intelligence, Turing test, brain, neurons, synapses
*   Brief history of AI
*   Terminology: Machine Learning (ML), Supervised vs. unsupervised learning, Neural Networks (NN), Deep Learning (DL), NLP, LLMs, Reinforcement Learning (RL).
*   Typical applications of AI. How to join the revolution?

![Image 3](https://www.graitechschool.co.il/wp-content/uploads/2022/12/Gilad-Kavalerchik__221220_146-e1671960198187.jpg)

Before AI - A quick review of some classical models

*   Basic statistics
*   Linear regression
*   PCA
*   K-means
*   Decision Trees
*   Imbalanced Class
*   Relevant python libraries: NumPy, Matplotlib, Pandas

![Image 4](https://www.graitechschool.co.il/wp-content/uploads/2022/12/Gilad-Kavalerchik__221220_58-e1671953361203.jpg)

Deep Learning

*   Main models and methods. Training, testing and validation.
*   Using Python Notebook and Colab.
*   Main AI training tools: Keras and TensorFlow by Google, Pytorch.
*   Hardware and software infrastructure: GPUs, Cuda, Dockers.
*   Relevant python libraries: NumPy, Matplotlib, Pandas.
*   Your first Neural Network model: Detecting patterns in tabular data.

![Image 5](https://www.graitechschool.co.il/wp-content/uploads/2022/12/%D7%90%D7%93%D7%99%D7%A1%D7%9C%D7%90%D7%9D-%D7%95%D7%90%D7%9C%D7%97%D7%90%D7%9F-scaled.jpg)

Computer Vision: CNNs and Vision Transformers (ViT)

*   Visual tasks: classification, detection, and segmentation.
*   Why is Machine Vision difficult?
*   NN building blocks and layers: single neuron, convolutions, pooling, fully connected layers, normalization, activation functions, loss.
*   Learning the model weights: the back-propagation algorithm.
*   NN architectures: feed-forward, recurrent, encoder-decoder, Siamese.
*   Practical open-source vision NNs: Resnet, Yolo, Clip, SAM.
*   Vision Transformers (ViT)
*   Native Multimodality
*   Computer Vision mini project: detecting objects in images

![Image 6](https://www.graitechschool.co.il/wp-content/uploads/2023/07/%D7%A2%D7%99%D7%A0%D7%AA-%D7%95%D7%93%D7%A0%D7%99%D7%90%D7%9C-scaled.jpg)

Natural Language Processing (NLP) using Transformers

*   Large Language Models: capabilities and limitations,and the shift towards Small Training Large Language Models (LLMs),Attention mechanism and the Transformer architecture.
*   Language Models (SLMs) for local execution (e.g., Llama, Gemma, Phi)
*   Using LLM services in your application: Google Vertex AI, Gemini APIs, and prompt engineering best practices
*   Parameter-Efficient Fine-Tuning (PEFT): LoRA and efficient adaptation techniques.
*   Retrieval-Augmented Generation (RAG): From basic RAG to advanced techniques (Hybrid Search, Re-ranking, GraphRAG).
*   Fine-tuning and Aligning LLMs: Instruction tuning, RLHF, and Direct Preference Optimization (DPO).
*   Parameter-Efficient Fine-Tuning (PEFT): LoRA,QLoRA, and integration with Hugging Face’s ecosystem.
*   NLP mini project: Building a business application using LLM APIs and a RAG architecture.

![Image 7](https://www.graitechschool.co.il/wp-content/uploads/2022/12/%D7%A0%D7%95%D7%A2%D7%94-scaled.jpg)

Deep Reinforcement Learning (RL)

*   Introduction: reinforcement learning vs. non-interactive supervised learning.
*   Methods: Q-Learning and Deep Q-Network.
*   Advanced Methods (Overview): Proximal Policy Optimization (PPO) and Actor-Critic models.
*   Applications: control and robotics, automated driving, finance, and the role of RL in GenAI(RLHF and Reasoning models).
*   Reinforcement learning mini project: gaming.

![Image 8](https://www.graitechschool.co.il/wp-content/uploads/2023/07/%D7%A2%D7%99%D7%A0%D7%AA-%D7%95%D7%93%D7%A0%D7%99%D7%90%D7%9C-2-scaled.jpg)

Other applications and trends

*   Time series forecasting in healthcare.
*   Recommendation systems.
*   Cyber security and authentication.
*   Generative AI Architectures:Diffusion Models and Flow-matching
*   Multimodal Generative AI: image, video and audio generation from text

![Image 9](https://www.graitechschool.co.il/wp-content/uploads/2022/12/%D7%99%D7%A4%D7%94-%D7%95%D7%93%D7%A0%D7%99%D7%90%D7%9C%D7%94-scaled.jpg)

Real-life AI projects in the industry

*   Overview of Hugging Face ecosystem: pretrained models, datasets, and Spaces.
*   Choosing an open source to start with.
*   Handling data: collecting, filtering, cleaning, augmenting, preprocessing.
*   Synthetic Data Generation: rare events, privacy, compliance. Synthetic Data Vault (SDV), Datagen (vision), GPT-based augmentation.
*   Validation, testing and and Observability: Measuring model quality, LLM-as-a-Judge, and evaluation frameworks (e.g., Ragas,LangSmith)
*   Using TensorBoard: Visualizing the training process, controlling convergence and overfit.
*   Experimenting architectures, loss functions and hyper-parameters.
*   Improving model speed and efficiency: optimization, models search, pruning, distillation, and Quantization (e.g., GGUF, EXL2)
*   Agentic Workflows and Multi-Agent Systems: Moving beyond basic agents using frameworks like LangGraph and CrewAI. Algorithmic benefits: tool-calling, memory, and reasoning loops (ReAct).
*   Hands-on: Build a basic research agent with memory and planning.
*   Deploying your AI models for smartphones and edge devices and NPUs with TF Lite
*   Deploying your AI models on Google Cloud Platform for scale and stability
*   AI development lifecycle with Google Cloud tools 

![Image 10](https://www.graitechschool.co.il/wp-content/uploads/2022/12/%D7%90%D7%A0%D7%92%D7%9C%D7%99-scaled.jpg)

Responsible AI

*       *   Personal, social, and economic implications
    *   Who is responsible? legal aspects
    *   Being fair and reducing biases
    *   Data privacy, compliance, and security
    *   Explainable AI
    *   AI Safety in Practice: Red Teaming, handling jailbreaks, and implementing Guardrails
    *   Dealing with deep fake
    *   Who owns the data, the models, and the code libraries?
    *   Dangers and opportunities for mankind
    *   Towards synergy of mankind and AI

*Changes may be applied in the Syllabus